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Entropy02:39

Entropy

34.5K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
34.5K
Entropy01:18

Entropy

3.4K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.4K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.1K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.1K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

4.6K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
4.6K

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Related Experiment Video

Updated: Dec 17, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Pattern Recognition via PCNN and Tsallis Entropy.

YuDong Zhang1, LeNan Wu2

  • 1School of Information Science and Engineering, Southeast University, P.R. China. zhangyudongnuaa@gmail.com.

Sensors (Basel, Switzerland)
|November 23, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new image processing technique using Pulse Coupled Neural Networks (PCNN) and Tsallis entropy for feature extraction. The method shows promise for object recognition, achieving a 72.5% classification rate in face recognition tasks.

Keywords:
Pattern recognitionTsallis entropyfeature extractionpulse coupled neural network

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Traditional feature extraction methods often struggle with variations in scale and translation.
  • The need for robust and efficient feature extraction techniques is critical in image recognition tasks.
  • Pulse Coupled Neural Networks (PCNN) and Tsallis entropy offer unique properties for signal and image analysis.

Purpose of the Study:

  • To propose a novel feature extraction method for image processing using PCNN and Tsallis entropy.
  • To develop a recognition method for isolated objects based on the proposed feature extraction.
  • To evaluate the performance and characteristics of the novel feature extraction method.

Main Methods:

  • Mathematical modeling of the PCNN.
  • Explanation of the fundamental concepts of Tsallis entropy.
  • Application of the combined method for feature extraction in image processing.
  • Parameter optimization using bacterial chemotaxis optimization (BCO) for face recognition.

Main Results:

  • The developed feature extraction method demonstrates independence from translation and scale.
  • Rotation independence was found to be slightly weaker at specific diagonal angles (45° and 135°).
  • The face recognition application achieved a highest classification rate of 72.5% using BCO for parameter tuning.

Conclusions:

  • The novel feature extraction method based on PCNN and Tsallis entropy is effective for image processing.
  • The method exhibits desirable properties like translation and scale independence, with potential for improvement in rotation invariance.
  • The application in face recognition shows acceptable performance and highlights the technique's practical value.